System and method for proactive motor wellness diagnosis based on potential mechanical faults

ABSTRACT

The present invention is directed to a system and method for proactively determining motor wellness arising from potential mechanical faults of the motor. A controller is configured to detect indicia of impending mechanical motor faults. The controller includes a processor configured to determine motor parameters of a given motor including a load and generate a set of baseline data for the given motor. The processor is also configured to acquire current spectrum data from the given motor during operation, map at least one from a plurality of load bins based on the load, and generate a mechanical fault signature from the current spectrum. The processor is caused to compare the mechanical fault signature to baseline data from the set of baseline data corresponding to the mapped bin and determine amplitude variances within the mechanical fault signature indicative of an impending mechanical fault prior to an actual mechanical fault occurrence.

CROSS-REFERENCE TO RELATED APPLICATION

This application claims the benefit of prior U.S. ProvisionalApplication Ser. No. 60/479,318 filed Jun. 18, 2003 and entitled METHODAND SYSTEM FOR IMPLEMENTING MOTOR DIAGNOSTICS AND SYSTEM WELLNESS.

BACKGROUND OF THE INVENTION

The present invention relates generally to motors and, moreparticularly, to a system and method for proactive detection ofconditions indicative of potential motor faults. Baseline data isgenerated by a wellness relay operating in a “learning” mode bymonitoring a given motor known to be operating under “healthy”conditions. After the “learning” mode is complete, the wellness relaymonitors the given motor and performs at least one of current signatureanalysis (CSA) and power signature analysis (PSA) to determine a motorfault index of the given motor. Specifically, frequency spectrumcomponents within carefully selected sidebands are summed and mapped toone of a plurality of load bins. By comparing the motor fault index tothe baseline data associated with the mapped load bin, the wellnessrelay detects conditions indicative of potential motor faults andcommunicates wellness alerts prior to an occurrence of a potential motorfault.

In North America, three-phase induction motors consume a largepercentage of all generated electrical capacity. Many applications forthis “workhorse” of industry are fan and pump industrial applications.For example, in a typical integrated paper mill, low voltage and mediumvoltage motors may comprise nearly 70% of all driven electrical loads.Due to the prevalence of these motors in industry, it is paramount thatthe three-phase motor be reliable. Industry reliability surveys suggestthat motor failures typically fall into one of four major categories.Specifically, motor faults typically result from bearing failure, statorturn faults, rotor bar failure, or other faults/failures. Within thesefour categories: bearing, stator, and rotor failure account forapproximately 85% of all motor failures.

This percentage could be significantly reduced if the driven equipmentwas properly aligned when installed, and remained aligned regardless ofchanges in operating conditions. However, motors are often coupled tomisaligned pump loads or loads with rotational unbalance and failprematurely due to stresses imparted upon the motor bearings.Furthermore, manually detecting such fault causing conditions isdifficult at best because doing so requires the motor to be running. Assuch, an operator is usually required to remove the motor from operationto perform a maintenance review and diagnosis. However, removing themotor from service is unsuitable in many industries because motordown-time is extremely costly and undesirable in many applications.

As such, some detection devices have been designed that generatefeedback regarding an operating motor. The feedback is then reviewed byan operator to determine the operating conditions of the motor. However,most systems that monitor operating motors merely provide feedback offaults that have already damaged the motor. As such, though operationalfeedback is sent to the operator, it is usually too late for preventiveaction to be taken.

Some systems have attempted to provide an operator with early faultwarning feedback. For example, vibration monitoring has been utilized toprovide some early misalignment or unbalance based faults. However, whena mechanical resonance occurs, machine vibrations are amplified. Due tothis amplification, false positives indicating severe mechanicalasymmetry are possible. Furthermore, vibration based monitoring systemstypically require highly invasive and specialized monitoring systems tobe deployed within the motor system

As such, other systems perform some signature analysis on feedback fromthe motor and attempt to detect deviations indicative of a fault. Whilethese systems may aid the operator in maintenance reviews of anoperating motor, they are typically invasive and require highlyspecialized sensors to monitor a specific motor application. That is,the detection devices are generally an autonomous unit with sensors thatmust be deployed around or within the motor. Therefore, the detectiondevices constitute another system that must be invasively deployedwithin the motor system and which is susceptible to faults anddeterioration. Additionally, connecting these specialized sensorsusually requires specialized tools, protective devices and/or clothingand highly skilled technicians because these sensors are intended to bedeployed to energized parts. Accordingly, while traditional monitoringdevices allow the operator to safely receive feedback regarding anoperating motor, the devices present additional autonomous systemsassociated with the motor which must be set-up, monitored, andmaintained. Therefore, traditional motor monitoring devices compound thecost of operating the motors.

Furthermore, such early fault warning feedback systems typically requiremultiple levels of configuration and tailoring to properly monitor aparticular motor and that motor within a particular application. Thatis, such systems must be individually configured to a specific motor,load, and application. For example, applications such as motor drivenfans and pumps are typically constant load applications. On the otherhand, applications such as conveyers or material handling applicationsare typically varying load applications. Generally, traditional earlyfault warning feedback systems must be manually calibrated not only forthe individual motor but also for the specific application within whichthe motor is operating. Therefore, traditional early fault warningfeedback systems require considerable investments in time andengineering to deploy the system.

Additionally, these systems must be regularly recalibrated toreconfigure the system for normal operational changes to the motor,load, and/or application, else risk false positives or negatives arisingfrom normal changes to the motor signature used for review. Suchrecalibrations must adjust for new load variances, changes to themotor-load configuration, changes in operational frequency, and newapplication variances, to name but a few. Therefore, while these earlyfault warning feedback systems may be capable of alerting an operator ofrequired maintenance, the systems alone may require maintenance andcorresponding downtime exceeding that of the monitored motor.

It would therefore be desirable to design a system and method tonon-invasively perform diagnostics on an operating motor that isspecific to that motor. Additionally, it would be desirable for thesystem and method to be implementable utilizing traditional motorsystems in order to avoid introducing additional autonomous sub-systems.Furthermore, it would be desirable that the system and method be capableof proactively diagnosing conditions indicative of a wide range ofpotential faults including mechanical faults and cavitation faults andbe able to alert an operator of an impending fault prior to an actualfault occurrence. Also, it would be advantageous that the system andmethod be capable of adjusting to a wide variety of motors, loads, motorsignatures, and applications and be capable of dynamically adjusting tonormal changes in the system over time.

BRIEF DESCRIPTION OF THE INVENTION

The present invention is directed to a system and method that overcomesthe aforementioned drawbacks. Raw data is acquired from a plurality ofsensors of a relay monitoring an operating motor. A baseline isdynamically generated to model the motor under known “healthy”conditions. Once the baseline is generated, the raw data withindynamically selected sidebands is processed and mapped to one of aplurality of load bins. Baseline data associated with the mapped bin isthen compared to the processed data to generate a fault index indicativeof potential faults for that particular operating motor. A proactivealert is then sent to an operator warning of a potential fault beforeany damage occurs.

In accordance with one aspect of the present invention, a controller isdisclosed that is configured to detect indicia of impending mechanicalmotor faults. The controller includes a processor configured todetermine motor parameters of a given motor including a load andgenerate a set of baseline data for the given motor. The processor isalso configured to acquire current spectrum data from the given motorduring operation, map at least one from a plurality of load bins basedon the load, and generate a mechanical fault signature from the currentspectrum. The processor is caused to compare the mechanical faultsignature to baseline data from the set of baseline data correspondingto the mapped bin and determine amplitude variances within themechanical fault signature indicative of an impending mechanical faultprior to an actual mechanical fault occurrence.

In accordance with another aspect of the present invention, a method ofdetecting impending mechanical faults is disclosed that includesgenerating baseline data for the operating motor, receiving current datafrom an operating motor, and performing at least one FFT on the currentdata to generate frequency spectrum data. The method also includesselecting system frequency sidebands within the frequency spectrum dataand summing the frequency spectrum data within the system frequencysidebands. The method includes comparing the summed frequency spectrumdata to a portion of the baseline data and determining amplitudevariances within a component of a running shaft speed of the motorindicative of prospective faults due to at least one of motormisalignment and unbalance.

In accordance with yet another aspect of the present invention, acomputer readable storage medium is disclosed having stored thereon acomputer program. The computer program includes instructions which, whenexecuted by at least one processor, cause the at least one processor toreceive operational current data from a motor, perform at least two FFTson the operational current data to generate frequency spectrum data, andaverage the frequency spectrum data. The at least one processor is alsocaused to generate a mechanical fault signature from frequency spectrumdata and, if in a learning mode, compile a baseline from the mechanicalfault index. However, if not in the learning mode, the at least oneprocessor is caused to determine a load of the motor and map the averagepower of the motor to a load bin based on the load of the operatingmotor. The at least one processor is also caused to compare a portion ofthe baseline corresponding to the mapped load bin to the mechanicalfault signature and determine an impending mechanical fault before afault occurrence.

Various other features, objects and advantages of the present inventionwill be made apparent from the following detailed description and thedrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate one preferred embodiment presently contemplatedfor carrying out the invention.

In the drawings:

FIG. 1 is a schematic representation of a motor assembly in accordancewith the present invention.

FIG. 2 is block diagram of a relay system in accordance with the presentinvention.

FIG. 3 is a graph illustrating a per unit motor current versus frequencyof a motor under normal operation.

FIG. 4 is a graph illustrating a per unit motor current versus frequencyof a motor operating in cavitation as identified in accordance with thepresent invention.

FIG. 5 is a graph illustrating linear averaging current values for alower sideband range of a motor under normal operation.

FIG. 6 is a graph illustrating linear averaging current values for anupper sideband range of a motor under normal operation.

FIG. 7 is a graph illustrating linear averaging current values for alower sideband range of a motor in cavitation as identified inaccordance with the present invention.

FIG. 8 is a graph illustrating linear averaging current values for anupper sideband range of a motor in cavitation as identified inaccordance with the present invention.

FIG. 9 is a graph illustrating instantaneous power values of a motorunder normal operation.

FIG. 10 is a graph illustrating noise within instantaneous power valuesof a motor indicating cavitation as identified in accordance with thepresent invention.

FIG. 11 is a graph of a per unit motor current versus frequency of amotor illustrating a current spectrum of motor shaft speed components.

FIG. 12 is a graph illustrating an example of the influence of energyleakage within a current spectrum of a motor.

FIG. 13 is a graph of current spectra of multiple motors with varyinglevels of misalignment operating without cavitation.

FIG. 14 is a graph of current spectra of multiple motors with varyinglevels of misalignment operating under cavitation.

FIG. 15 is a graph of power spectra of multiple motors with varyinglevels of misalignment operating without cavitation.

FIG. 16 is a graph of power spectra of multiple motors with varyinglevels of misalignment operating under cavitation.

FIG. 17 is a block diagram illustrating an overview of a technique forpredictive fault detection in accordance with the present invention.

FIG. 18 is a detailed flow chart illustrating the steps of a techniquefor predictive fault detection in accordance with the present invention.

FIG. 19 is a flow chart illustrating the steps of a technique forpredictive cavitation fault detection in accordance with the presentinvention.

FIG. 20 is a flow chart illustrating the steps of a technique forpredictive mechanical fault detection in accordance with the presentinvention.

FIG. 21 is a more detailed flow chart illustrating the steps of atechnique for predictive fault detection in accordance with the presentinvention.

FIG. 22 is a continuation of the flow chart of FIG. 21 illustrating thesteps of a technique for predictive fault detection in accordance withthe present invention.

FIG. 23 is a continuation of the flow chart of FIG. 22 illustrating thesteps of a technique for predictive fault detection in accordance withthe present invention.

FIG. 24 is a continuation of the flow chart of FIG. 23 illustrating thesteps of a technique for predictive fault detection in accordance withthe present invention.

FIG. 25 is a block diagram of a communications system for communicatingproactive alerts in accordance with the present invention.

FIG. 26 is a representation of a graphical user interface in accordancewith the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT

The present invention is related to the detection of abnormal conditionsto predictively determine potential motor faults. Current signatureanalysis (CSA) or power signature analysis (PSA) is utilized to reviewraw data received from a plurality of sensors of a relay monitoring anoperating motor. The system, which is disposed within the relay, mapsraw data within dynamically selected sidebands to a bin and generates afault signature index from a comparison of the raw data to baseline dataassociated with the mapped bin. An operator of the monitored motorsystem is then proactively alerted of a potential fault prior to a faultoccurrence.

Referring now to FIG. 1, a motor assembly such as an induction motorconfigured to drive a pump is shown. The motor assembly 10 includes amotor 12 that receives power from a power supply 14. The motor assembly10 also includes a relay assembly 16 used to monitor as well as controloperation of the motor in response to operator inputs or motor faultconditions. The motor 12 and the relay assembly 16 typically are coupledto electronic devices such as a power control/start 17 in series withthe motor supply to control power to the motor 12. The relay assembly 16includes a processor 18 that, as will be described in greater detailwith respect to FIG. 2, implements an algorithm to determine thepresence of unwanted mechanical conditions and predictively alert anoperator of a potential fault before a fault occurs. The relay assembly16 further includes at least a pair of voltage sensors 20 and a pair ofcurrent sensors 22. As is generally known, voltage and current data maybe acquired from only two of the phases of a three-phase motor asvoltage and current data for the third phase may be extrapolated fromthe voltage and current data of the monitored two phases. While thepresent invention will be described with respect to a three-phase motor,the present invention is equivalently applicable to a two-phase and asingle-phase motor.

Referring now to FIG. 2, a more detailed block diagram of the relayassembly 16 of FIG. 1 is shown. As stated with respect to FIG. 1, therelay assembly 16 includes a processor 18 and sensors 20, 22.Furthermore, the relay assembly 16 includes a raw data storage 24, anotch filter 26, and a gain adjuster 28. As will be described in detailwith respect to FIGS. 17, 19, and 20, these systems 24, 26, 28, operateto receive the raw data generated by the sensors 22, 24 and prepare theraw data for processing by the processor 18. However, as will bedescribed, some raw data may also be passed to the processor 18 forfunctions such as traditional overload protection and metering. That is,it should be recognized that the relay assembly 16 includes a systemcapable of performing both traditional relay functions as well aswellness functions. As a further example, relay may be configured toperform motor start or stop functions, perform trip and reset functions,or monitor motor phase current and thermal capacity.

Specifically, the processor 18 is configured to control at least twodistinct functionalities including traditional overload and meteringfunctions 30 and current signature analysis (CSA)/power signatureanalysis (PSA) functions 32 for wellness based monitoring based on pumpcavitation 34 and misalignment unbalance 36. Current signature analysisand PSA involve analysis via a fast Fourier transform (FFT) of motorcurrents or instantaneous power to identify equipment or systemabnormalities. As will be described in detail, this approach involvescomparison of a known “good” current or power signatures recorded duringnormal operation to the actual running current or power signature. Theknown “good” current or power signature is stored as a model 32. Themodel is then compared during at least one of a pump cavitation analysis32 or misalignment/unbalance analysis 36 performed by the processor 18.Cavitation of a pump, such as a centrifugal pump, occurs when the pump'sinlet static pressure drops below the liquid vapor pressure, whichsubstantially increases the probability of motor failure.

The relay assembly 16 also includes a communications interface 38 forconnection to a communications bus 40 of a traditional communicationssystem associated with the motor being monitored. Therefore, as will bedescribed in greater detail with respect to FIG. 26, the communicationsinterface 38 is designed to integrate the relay assembly 16 withexisting communications systems such that the relay assemblyincorporates seamlessly into the existing communications system and noadditional or proprietary communications system is necessary. That is,should the processor 18 detect operation of the motor deviating from thecurrent signature or power signature model 32, within a user prescribedtolerance, a proactive alert is sent via the communications interface 38onto the communications bus 40. Therefore, the communications interface38 is designed to allow the communication of the proactive alerts to anoperator without the need for any additional communicationsinfrastructure.

Therefore, the relay assembly 16 operates as a motor wellness relay thatperforms condition based monitoring (CBM) that is non-invasive in natureand also operates as a traditional overload/metering relay. The motoroperation is not disturbed while raw data collection and processingoccurs. Additionally, all systems and components necessary for thepredictive fault detection technique, as will be described in detailbelow, are integrated within a relay housing 42. The relay assembly 16is only marginally larger than traditional relays that are incapable ofsophisticated predictive fault detection. Due to the size of the relayassembly 16, the relay assembly is immediately retrofittable intoexisting systems utilizing traditional solid-state relays withoutpredictive fault detection. For example, critical process applicationssuch as boiler feed pumps, may benefit from additional diagnosticcapabilities of the relay assembly 16, enabling operators to schedulemaintenance based on the sensed condition of the equipment before animpending failure occurs. Since the relay assembly 16 is approximatelythe size of traditional relay, the relay assembly 16 may be configuredas a kit that is able to readily replace any existing relay that may beconfigured to monitor such a boiler feed pump. It is contemplated thatthe relay assembly 16 may be useable with a wide variety of motorshaving various horsepowers (HP) and a variety of poles.

Referring now to FIG. 3, a graph illustrating a sensed per-unit motorcurrent versus frequency of a motor under normal operation, such ascould be determined by the relay assembly 16 of FIG. 2, is shown.Multiple consecutive FFT processings, preferably four, of the raw dataare performed and averaged to produce a relatively stable currentsignature 50. For example, a motor driving a centrifugal pump may havebeen monitored during normal operation to produce a current signaturesuch as shown in FIG. 3. Accordingly, a trace for the same motor andcentrifugal pump, this time with the pump operating in a cavitationstate, could produce a current signature such as shown in FIG. 4. FIG. 4shows that there is a measurable difference in motor current spectrum 51compared to the current signature 50 of the pump operating in a normalstate, as shown in FIG. 3. Specifically, the cavitation, as well asother mechanical faults, can produce certain noise patterns and specificfrequency components in the motor current spectrum, which are used forfault indication or signature. However, the specific noise patternsdiffer greatly from motor to motor or from one operating environment toanother. Additionally, mechanical forces resulting from potential faultconditions are typically highly dependent on the rotational speed of themotor. Therefore, accurate detection of a noise pattern indicative of apotential fault can be difficult while avoiding false positives andnegatives. As will be described, false positives and negatives areavoided by using CSA and/or PSA and tailoring the analysis to anindividual motor through the use of dynamically adjusted sidebands andload bins.

Referring again to FIGS. 3 and 4, the noise levels around the systemfrequency, designated f_(base), are considerably higher for thecavitation mode illustrated in FIG. 4 as opposed to FIG. 3. As will bedescribed, by reviewing the noise pattern in the current spectrum of themotor being monitored with respect to the system frequency in a definedrange can aid in the determination of a predictive fault indicatorsignature. Two sidebands of the system frequency are defined to aid indetection, designated as the lower sideband (LSB) and the upper sideband(USB).

By integrating all noise energy in a frequency sideband extending fromof f1 through f2 for the LSB and f3 through f4 for the USB around thesystem frequency and comparing data between FIG. 3 and FIG. 4, apotential or impending motor fault can be detected, as will bedescribed. However, to avoid false positives and/or negatives due tovariations in motors, a learning process is utilized to generatebaseline data associated with a plurality of load bins for thecomparisons that are specifically tailored to a particular motor. Aswill be described, the magnitudes of the frequency sidebands are notconsistent for all load levels. For example, when detecting mechanicalfault conditions, when there is mechanical resonance, the value of thesummed frequency components within the sidebands will typically behigher than associated levels when resonance is not present. As will bedescribed, the plurality of load bins compensates for load relatedabnormalities and resonance by classifying load levels into bins andcalculating a specific baseline for each load bin.

Integration of the noise energy within the LSB and USB can be anarithmetic sum for all frequency components in each respective sideband.It is also contemplated that an averaged value can be used, which can beobtained by dividing the integration result by the number of noisecomponents in the frequency sideband.

In a simplified manner and according to one embodiment, linear averagingaround system frequency is achieved for pump cavitation detectionaccording to: $\begin{matrix}{{{{LSB}:\quad E_{L}} = {\frac{1}{f_{2} - f_{1}}{\sum\limits_{f = {f1}}^{f2}\quad{i(f)}}}},} & \left( {{Eqn}.\quad 1} \right)\end{matrix}$ $\begin{matrix}{{{{USB}:\quad E_{U}} = {\frac{1}{f_{4} - f_{3}}{\sum\limits_{f = {f3}}^{f4}\quad{i(f)}}}},} & \left( {{Eqn}.\quad 2} \right)\end{matrix}$  Whole energy integration: E=E _(L) +E _(U)  (Eqn. 3),where i(f) represents the noise components at defined frequencies withinthe frequency bins.

FIGS. 5 and. 6 show the results of the linear averaging values of theLSB and USB of FIG. 3, respectively, around a system frequency of 60 Hz.Four traces 52, 54, 56, and 58 indicate different misalignmentconditions at the various load points. From the traces 52-58, a baselinefor known acceptable pump operation is generated. With the outlet valveof a pump being driven by the monitored motor closed across the nine (9)different load steps, there is no condition where an indication ofcavitation exists. Accordingly, the four traces 52-58, representdifferent levels of parallel misalignment ranging from no misalignmentin trace 52 to 30 millimeters (mm) of misalignment in trace 58. As canbe discerned from the traces 52-58, all of the curves are relativelyflat with respect to different loads and misalignment conditions.However, it should be noted that the current deviation for each trace52-58 varies according to each load step, which could lead to falsepositive or negative fault indications. Therefore, as will be described,a plurality of load bins is utilized to alleviate the potential of falsepositives and negatives.

Referring now to FIG. 7 and FIG. 8, the same load points are shown as inFIG. 5 and FIG. 6, except that now the monitored motor is operatingunder cavitation. As can be seen in both FIG. 7 and FIG. 8, across thedifferent levels of parallel misalignment in the traces 52-58, the noiseintegration values are significantly higher when compared to thebaseline shown in FIGS. 5 and 6.

While FIGS. 5-8 illustrate the determination of cavitation within thecurrent spectrum of a motor, FIGS. 9 and 10 illustrate the determinationof cavitation within the instantaneous power of a motor. FIG. 9 shows agraph of integrated power spectra for a motor operating under normalconditions. On the other hand, FIG. 10 shows the integrated powerspectra for the same motor operating under cavitation. As in the motorcurrent, three-phase instantaneous motor power also contains spectrasignature information relating to cavitation phenomena. As can be seenin FIGS. 9 and 10, similar results may be obtained for cavitationdetection through instantaneous power as through current analysis, as inFIGS. 3-8.

It should be noted that the major part of the instantaneous power isreal motor power, which is a DC component, and all the other frequenciesare left or right shifted within the spectra for the positive ornegative components in terms of three-phase quantities. The cavitationnoise sideband must be selected above DC frequency. Therefore, the noiseband integration for cavitation detection may be accurately performed ata lower sideband frequency range compared to the current signaturemodel. It should be noted that in motor voltage and currentmeasurements, there are always some DC components in sampled data due tosignal conditioning circuit errors. Therefore, a specially designed highpass filter is used to remove the unwanted DC components and itsneighboring frequencies to assure the accuracy of the data.

Additional analysis may also be performed to predicatively detect motorfaults resulting from misalignment or imbalance. Referring now to FIG.11, a graph of per-unit motor current versus frequency of a motorillustrating a current spectrum of motor shaft speed components isshown. As will be shown, mechanical faults may be identified from motorshaft speed components. Mechanical faults are defined as unbalanced loador misalignment, including radial and angular misalignment. These faultscan be detected by monitoring amplitude variances of specific shaftrunning speed components in the motor current spectrum. There are manyshaft speed related components in the current spectrum. FIG. 11 shows acurrent spectrum of shaft speed related components around the basefrequency (f_(base)) for a two-pole motor.

Specifically, sidebands, labeled LSB and USB are selected surroundingthe running frequency of the motor within which the desired shaftrunning speed component amplitudes are reviewed. In a simplified mannerand according to another embodiment, the frequency components are summedfor mechanical fault detection according to: $\begin{matrix}{{{{LSB}:\quad E_{L}} = \sqrt{\sum\limits_{f = {f1}}^{f2}\quad{i(f)}^{2}}},} & \left( {{Eqn}.\quad 4} \right)\end{matrix}$ $\begin{matrix}{{{USB}:\quad E_{U}} = {\sqrt{\sum\limits_{{f`} = {f3}}^{f4}\quad{i(f)}^{2}}.}} & {\left( {{Eqn}.\quad 5} \right)\quad}\end{matrix}$Where i(f) is all the frequency components within the selected sideband.For mechanical fault detection E_(L) and E_(U) are then linearlyaveraged. It should be noted that the sidebands selected for cavitationfault detection and mechanical fault detection differ significantly. Forexample, it is not uncommon that a cavitation fault detection sidebandmay span approximately 15 Hz while a mechanical fault detection sidebandmay span approximately 2 Hz. Simply, as will be described, the sidebandselection criteria for determining each fault type differs.

While FIG. 11 shows a current spectrum of shaft speed relatedcomponents, accurately measuring the amplitude of a specific frequencycomponent in the current spectrum may be hampered by energy leakage.That is, since an FFT resolution must be limited to maintain manageabledata ranges, accurate representation of the acquired data may bedifficult because not all frequency components accurately represent realamplitudes in the spectrum due to energy leakage. Accordingly, for thosecomponents with frequencies not at FFT resolution steps, amplitudes aresmaller than their actual values. FIG. 12 illustrates an example of theinfluence of energy leakage in the current spectrum with 0.1 Hzresolution for signals with amplitude of unity (1) and frequenciesincluding f₁ Hz, enumerated 60; f₁+0.03 Hz, enumerated 62; f₁+0.05 Hz,enumerated 64; f₁+0.07 Hz, enumerated 66; and f₁+0.1 Hz, enumerated 68.The amplitudes of signals with frequencies not at FFT resolution steps,for example, the signal at f₁+0.05 Hz 64, are less than unity (1) in theFFT spectrum.

In the motor current spectrum, frequencies of the interested shaft speedcomponents could be any value around a harmonic of system frequency. Inmost cases, these components are not necessarily at FFT resolution stepsand the amplitudes of these components are relatively small. Sincemechanical fault detection is based on monitoring amplitude variances ofspecific shaft speed components, accurate amplitude estimation for thesecomponents is required.

Therefore, two techniques are contemplated to estimate the amplitude ofshaft speed related components in the current spectrum. According to oneembodiment of the invention, a square root of accumulated squared valuesfor all points in the above-described narrow spectral sideband aroundthe shaft speed component is calculated. According to this embodiment, aroot sum square (RSS) value is calculated for all frequency componentsin a selected frequency band around the shaft speed frequency as anestimated value. A start and end frequency of the selected sideband inthe current spectrum are used that cover the desired shaft speedcomponent. To achieve improved estimation performance, a sideband isselected in a way that the interested shaft speed frequency is centeredabout this defined frequency band. Accuracy of this estimation dependson the size of the selected bin, such that the wider the selected bin,the more accurate the estimation. However, wider bin selection canresult in errors in estimation if a major neighboring frequencycomponent is close to or falls within the selected bin because theenergy of this component will be accumulated in the summed value.

To overcome these inherent limitations in the RSS technique and inaccordance with a preferred embodiment, the amplitude may be estimatedby utilizing three consecutive points around the shaft speed component.In this case, polynomial estimation is used to estimate the shaft speedamplitude. For example, assuming i_(k) is the interested shaft speedcomponent and i_(k)+1 and i_(k)−1 are the next upper and lowerneighboring components, then the estimated amplitude of shaft speed isgiven by:Ampl=i _(k) +C×|i _(k+1) −i _(k−1)|  (Eqn. 6),where C is a constant selected based on the specific FFT resolutionapplied. Utilizing this technique renders improved estimation over theaforementioned RSS technique and only uses three consecutive frequencyvalues. Therefore, the major neighboring frequency components close tothe shaft speed frequency have little effect on estimated values.

Table I compares the performance of both amplitude estimation techniquesto FFT results. Table I clearly shows that the FFT analysis yields theamplitude error due to energy leakage. Simply, the FFT amplitudes foreach signal frequency are below unity (1). For example, the middle ofthe FFT steps (f₁+0.05 Hz) includes an error of 36.31%. On the otherhand, the RSS estimation technique includes a significantly improvedlargest error of 3.41% at f₁+0.05 Hz. However, over the entire frequencyspectrum, the polynomial estimation yields a largest error of only0.279% at f₁+0.03 Hz. Therefore, the polynomial estimation techniqueachieves superior accuracy. TABLE I AMPLITUDE ESTIMATION SIMULATIONRESULTS Signal FFT RSS Polynomial Frequencies (Hz) AmplitudesEstimations Estimations f₁ 0.999941 0.999941 0.999941 f₁ + .01 0.983660.997289 1.00042 f₁ + .02 0.935556 0.989701 1.00169 f₁ + .03 0.858390.9795 1.00279 f₁ + .04 0.756677 0.970364 1.0022 f₁ + .05 0.6368170.965892 0.997572 f₁ + .06 0.757035 0.969236 1.00195 f₁ + .07 0.85850.977782 1.00272 f₁ + .08 0.93564 0.988217 1.00175 f₁ + .09 0.9835350.996648 1.00051 f₁ + .1 0.999941 0.999941 0.999941

Thus, unbalanced motor loads or misalignments, including radial andangular misalignment, can be detected by monitoring amplitude variancesof specific shaft running speed components in the motor current spectrumwithin carefully selected sidebands. The above-described polynomialestimation technique is preferred to compensate for energy leakage. FIG.13 and FIG. 14 show results using the polynomial estimation techniquefor multiple motors including various levels of misalignment with andwithout cavitation, respectively, using the current spectra.Specifically, the four traces 60-66 indicate different misalignmentconditions including no misalignment at trace 60, 10 mm of misalignmentat trace 62, 20 mm of misalignment at trace 64, and 30 mm ofmisalignment at trace 66 at various load steps. Similarly, FIG. 15 andFIG. 16 illustrate the derivation of similar results for theidentification of radial misalignment with and without cavitation,respectively, using the power spectrum.

As is apparent from FIGS. 15 and 16, the motor power analysis yieldsfault indicators for all corresponding load levels and fault conditionsthat are comparable to the notched current analysis illustrated in FIGS.13 and 14. It should be noted that as the load varies from load step 0to load step 9, the current range of the motor varies significantly. Forexample, trace 64 of FIG. 13 begins at a summed sideband current ofgreater than 20 at load step 0 and drops to a summed sideband current ofless than 10 at load step 9. Accordingly, as will be described, aplurality of load bins is used such that the summed sideband data at agiven load is compared to baseline data at a corresponding load. Theload bins allow detection even though the spectral components associatedwith conditions indicative of potential mechanical faults do not alwayslinearly change as the load varies. For example, mechanical resonance inthe coupled systems may affect the spectral components to increasedrastically; however, the plurality of load bins and dynamicallyselected sidebands alleviate the potential for false positives arisingfrom these conditions.

It should be recognized that obtaining accurate results using theinstantaneous power spectra requires the removal of the DC componentinherent in the system voltage and current of the motor. If the DCcomponent in voltage and current measurement is not removed, there willbe a large fundamental component in the power spectrum. Accordingly, ifthe system frequency varies and deviates from FFT steps, there will be asignificant FFT energy leakage, which will raise all the neighboringfrequency component levels. In some cases, this energy leakage may be sosignificant that it buries the useful shaft speed component. In thiscase, detection of the shaft speed component becomes difficult orimpossible. Therefore, as will be described, a filter is used to removethis DC component.

FIG. 17 is a block diagram of a system 100 utilized to implement atechnique for predictive fault detection in accordance with the presentinvention. Before describing the system 100, it should be recognizedthat while the blocks of the system 100 will be described as modules,all processing is performed by the relay processor 18 of FIG. 2.Therefore, though described as independent modules, the steps performedby the modules are accomplished by the relay processor 18 of FIG. 2.

Referring now to FIG. 17, a plurality of relay sensors 101 is configuredto monitor the operation of a motor and generate voltage data 102 andcurrent data 104. In accordance with a preferred embodiment, at leastone phase 106 of the current data 104 is adaptively filtered by a notchfilter 108 to derive desired current data. All data 102-106 is passed toa data acquisition unit 110 to derive the necessary power sensing moduledata including voltage and current measurements, as well asinstantaneous power computations. The data acquisition unit 110 passesthe three-phased power data 112 and notched current data 114 to a lowpass filter module 116. The low pass filter module 116 serves to cut offfrequencies below 120 Hz as well as anti-alias and decimate the notchedcurrent data 114. After filtering, the data 112, 114 is passed to a dataprocessing module 118 that computes a mean power from the three-phasepower data 112 to check load conditions and transients within the meanpower. If the load and the transients are above a threshold, theprocessing module 118 performs an FFT on the notched current data 114and calculates a running average for the current spectrum and power.

After data processing by the processing module 118, the processed data120 is passed to a fault signature analysis module 122 to generate amotor fault signature. As will be described in greater detail withrespect to FIGS. 19 and 20, a cavitation fault signature (CFS) and/ormechanical fault signature (MFS) are calculated to determine the overallwellness of the motor.

To generate the CFS, the fault signature analysis module 122 calculatesa median filtered frequency spectrum and then accumulates all spectrumcomponents in sidebands within the frequency spectrum based on motorparameters, specifically, a system frequency (f_(s)). The sidebands aredefined with respect to the f_(s). For example, the lower sideband mayrange from f−25 Hz to f_(s)−5 Hz and the upper sideband may range fromf_(s)+25 to f_(s)+5. The components with the sidebands are then summedas previously described. The fault signature analysis module 122 thensaves this value as the CFS.

To generate the MFS, the fault signature analysis module 122 determinessideband ranges within the frequency spectrum based on motor parameters.In accordance with a preferred embodiment, the sidebands are selectedsuch that:f _(sideband) =|k*f _(e) ±m*f _(r)|  (Eqn. 7),where f_(r) is the rotating frequency of the motor, and k and m areintegers selected during configuration based on the motor parameters.

Once the sidebands are selected, all spectrum components are summedwithin the sidebands to form the MFS. Specifically, the MFS is summedacross all spectrum components (ik) such that:MFS={square root}{square root over (Σi ₂ ²)}  (Eqn. 8).Once calculated, the fault signature analysis module 122 then saves thisvalue as the MFS.

The MFS and CFS are then prepared by a diagnosis preparation module 124.Specifically, the averaged power is mapped into one of a plurality ofbins to determine a monitoring state of the system. In a preferredembodiment, there are seven load bins to allow for a wide data range.Additionally, each load bin may correspond to one of two states. Thestates correspond to whether the system is in a learning mode or amonitoring mode.

If the currently mapped bin state corresponds to the learning mode, thedata is passed to a baseline module 126 to perform linear averaging onthe CFS and MFS with previously stored baseline data for the selectedload bin. Once the linear averaging is complete, the data is stored as aCFS baseline and an MFS baseline that is associated with the currentlymapped load bin. The baseline module 126 then determines whethersufficient baseline data has been generated for the current load bin.Specifically, as will be described, if the baseline module 126determines sufficient iterations have occurred to generate robustbaseline data for the currently mapped load bin, the state of theassociated load bin is switched to the monitoring mode, else, the loadbin remains in the learning mode. In either case, raw data is againacquired 110 and the system reiterates.

On the other hand, if the currently mapped load bin corresponds to themonitoring state, the CFS and MFS are sent to a diagnostic evaluationmodule 128. The diagnostic evaluation module 128 compares the CFS andMFS to the baseline data corresponding to the currently mapped load bin.If either the CFS or MFS differs from the baseline data by greater thana threshold, a potential fault has been identified and a fault flag iscommunicated through a fault communication interface 130. However, aslong as the CFS or MFS does not exceed the baseline data by greater thanthe threshold, the data acquisition unit 110 continues to gather dataand the processing routine reiterates.

An overview of this process is illustrated in FIG. 18. Specifically,three-phase current and voltage data are received from relay sensors140. One phase of the current data is selected and the fundamentalfrequency is dynamically removed 142. The technique continues byperforming traditional motor metering functions 144 and overload relayoperations 146 based on the data received 140. That is, it iscontemplated that wellness monitoring and traditional relay functions,such as metering 144 and overload operation 146, may be performed on thesame data. Therefore, the system acquires traditional relay-type data140 to perform both traditional relay functions 144, 146 and thefollowing wellness functions.

The data is then used to perform at least one of CSA and PSA to generatea CFS and/or MFS 148. The CFS is compared to a CFS baseline to detectstep changes in frequency decibel (dB) levels within system frequencysidebands to identify motor operation under cavitation 150. The detailedsteps of the process to identify motor operation under cavitation 150will be described with respect to FIG. 19. Additionally, the MFS iscompared to an MPS baseline to detect step changes in frequency dBlevels in running frequency sidebands to identify mechanical faults 152.The detailed steps of the process to identify mechanical faults 152 willbe described with respect to FIG. 20.

The results of the comparisons 150, 152 are then stored in registers foroperator access 154. The stored data is then compared to an acceptabletolerance range 156 to determine whether the motor is operating outsidethe acceptable range, which is indicative of impending faults. If thedata is not outside the acceptable range 158, the system reiterates anddata acquisition from the relay sensors continues 140. However, if thedata is outside the acceptable range 160, the relay sends a proactivealert 162 to indicate to an operator that the motor is operating underconditions of an impending mechanical fault or a cavitation fault anddata acquisition from the relay sensors continues 140.

Referring now to FIG. 19, the steps of a technique 163 for identifyingpump cavitation are shown. This technique provides a pump/machineoperator with an indication that the pump is operating in a reduced flowmode and/or is cavitating. The technique 163 begins by receiving rawdata from the relay sensors 164. The inputs to the cavitation detectiontechnique 163 are a “notched” current signal, the system frequency, anda real power measured in watts expressed as a percentage of full load.The cavitation identification 163 can conceptually be identified ashaving two major components. The first component is the data acquisitioncomponent. To implement the first component, the data is notch filtered166 to maximize the fidelity of the data and subsequently digitized 168for processing. The digitized data 168 is then decimated to acquire thecorrect resolution 170.

Specifically, the acquisition component is responsible for notchfiltering and applying a decimation filter to the “notched” currentsignal. Decimation is required so that a high-resolution frequencyspectrum of input signal over the bandwidth of interest can be computedwith a reasonable length FFT. Since the effects that cavitation producesin the motor current can be quite small (less than 1% of the magnitudeof the fundamental frequency component), false positive detections ofcavitation could result from small increases to the signal noise floor.The signal noise floor may vary over time due to any number ofenvironmental factors and/or normal aging of a motor (e.g. bearingwear). In attempt to avoid false positive detections, the acquisitioncomponent will filter the “notched” current signal with an adaptivedigital comb filter 171 that adjusts nulls based on the system frequencyto further remove the fundamental and any harmonics present in thesignal. As will be described, if in a learning mode, it is contemplatedthat an RMS of the output of the adaptive filter may be computed as anestimate or averaged with baseline for use by the upcoming monitoringcomponent of the cavitation technique 163.

However, if not in the learning mode, the monitoring component of thecavitation technique 163 is then initiated. The monitoring componenttakes the output of the acquisition component and attempts to determineif the data that was acquired is of sufficient quality to make anestimate of the cavitation fault index. As a basic conditions that mustbe satisfied by the input data to the cavitation detection algorithm,the data must be relatively stable during the entire FFT analysis period172. To determine whether the signal is sufficiently stable 172, thedecimated notch samples are applied to a module that computes statisticson the samples. The statistics are compared to predeterminedthreshold(s) to detect significant jumps in the signal statistics andidentify the existence of outliers. If statistical abnormalities aredetected 173, the data is not used for further processing by thecavitation detection technique, the technique reiterates, and new datais acquired 164. However, if no jumps or outliers are identified thedata frame will be used 174 to compute a cavitation fault indexmeasurement through an FFT 175.

Specifically, once stable data has been approved 174, the monitoringcomponent computes the magnitude spectrum of the decimated notch currentusing an FFT algorithm 175. In accordance with a preferred embodiment, aplurality of FFTs, preferably at least four, is then performed on thedata 175. The cumulative magnitude spectrum is then updated with the newdata through a power estimate computed by summing the cumulativemagnitude spectrum over the frequency range of interest and averaging176. The average FFT results 176 are then used to produce a sum of noiseenergy within sidebands of the system frequency of the motor beingmonitored. For example, for a motor with a system frequency of 60 Hz,sidebands are selected on either side of the 60 Hz frequency range andthe frequency components within the side bands are summed to form acavitation fault signature which is compared to the baseline data 177.From this comparison, a cavitation fault index is computed as the ratioof the energy in the frequency sidebands of the new spectrum divided bythe energy in the frequency sidebands of interest previously accumulatedduring a learning period (baseline) 177.

The radio of summed noise energy to baseline data (cavitation faultindex) is then compared to a threshold to determine whether it deviatesfrom the threshold 178. The threshold is particular to the motor and maybe input by the operator, dynamically generated simultaneously with thebaseline data, or predetermined such as from a lookup table. If thecavitation fault index is not greater than the threshold 180, nocondition indicative of future motor faults due to pump cavitation hasbeen detected, and the system continues to receive raw data from therelay sensors 164. However, if the cavitation fault index is greaterthan the threshold 182, the system automatically sends a proactive pumpcavitation alert 184 to alert the operator that a condition has beendetected that indicates an impending fault due to pump cavitation.

The above-described cavitation detection technique is applicable tothree-phase induction motor driven pumps. The diagnostic function isaccomplished via spectral analysis of the notched motor current signalacquired from the motor terminal currents by traditional relay sensors.No other or additional instrumentation beyond that which is found is arelay is required.

Referring now to FIG. 20, the steps of a technique 186 for determiningconditions indicative of impending mechanical faults are shown. Beforedescribing the technique, it should be noted that the amplitude of thespectral sidebands when detecting mechanical faults are more dependenton the loading of the motor. With no-load, the spectral sidebands arequite high even on a well-aligned motor. These peaks are due to theinherent machine and instrumentation asymmetries unique to eachinstallation of a motor system. As the load increases, these spectralpeaks are dampened. When loaded, as the degree of misalignmentincreases, the amplitude of the sidebands also increases. Therefore, itis noted that that the motor should be loaded above 50% and that themonitoring of the spectral sidebands is done on a per load basis.

As in the cavitation detection technique described with respect to FIG.19, the misalignment identification technique 186 can conceptually beidentified as having two major components. The first component is thedata acquisition component. The inputs to the misalignment detectiontechnique 186 are the same “notched” current signal, system frequency,and real power 188 used for cavitation detection. The data acquisitioncomponent is responsible for notch filtering and applying a decimationfilter to the “notched” current signal 190. Digitizing 192 anddecimation 194 are required so that a high-resolution frequency spectrumof input signal over the bandwidth of interest can be computed with areasonable length FFT. Since the effects that misalignment produces inthe motor current can be quite small with respect to the fundamentalfrequency of the motor, false positive detections of impendingmechanical faults could result from small increases to the signal noisefloor. The noise floor may vary over time due to any number ofenvironmental factors and/or normal aging of a motor due to the learningcapabilities of the wellness model. In an attempt to avoid falsepositive detections, the acquisition component will filter the “notched”current signal with an adaptive digital comb filter 195 that adjustsnulls based on the system frequency to further remove the fundamentaland any harmonics present in the signal.

As previously described, energy leakage correction 196 is performed suchthat the real portions of the necessary components of the frequencyspectrum of the data are properly discernable. Then, followingcorrection for energy leakage 196, the technique 186 attempts todetermine if the data that was acquired is of sufficient quality to makean estimate of the misalignment fault index. One of the basic conditionsthat must be satisfied by the input data to the misalignment detectionalgorithm is that the data is relatively stable during the entire FFTanalysis period 197. To determine whether the signal is sufficientlystable 197, the decimated notch samples are applied to a module thatcomputes statistics on the samples. The statistics are compared topredetermined threshold(s) to detect significant jumps in the signalstatistics and identify the existence of outliers. If statisticalabnormalities are detected 198, the data is not used for furtherprocessing by the misalignment detection technique, the techniquereiterates, and data is reacquired 188. However, if no jumps or outliersare identified the data frame will be used 199 to compute a misalignmentfault index measurement through a FFT 200.

It is contemplated that an RMS of the output of the adaptive comb filtermay be computed as an estimate of or averaged with the baseline for useby the upcoming monitoring component of the misalignment technique 186.If the system is not in a learning mode, once stable data has beenapproved 199, the monitoring component computes the magnitude spectrumof the decimated notch current using an FFT algorithm 200. In accordancewith a preferred embodiment, a plurality of FFTs, preferably at leastfour, is performed on the data 200. The cumulative magnitude spectrumwithin the desired sidebands is then updated with the new data throughan estimate computed by summing the cumulative magnitude spectrum overthe frequency sideband ranges of interest and averaging 202. That is,the average FFT results 202 are then used to produce a sum of noiseenergy within sidebands of the system frequency of the motor beingmonitored, called a mechanical fault signature. This mechanical faultsignature is then compared to the baseline to determine a deviationtherefrom 203. As will be described in detail below, this number isreferred to as the mechanical fault index 203.

The mechanical fault index is then compared to a threshold to determineif the mechanical fault signature deviates significantly from thebaseline data by greater than the threshold 204. If not 206, the systemcontinues to receive and process raw data 198. However, if the variancesdo significantly deviate from the baseline data by greater then thethreshold 208, the system sends a proactive mechanical fault alert 210to indicate that the system has detected conditions indicative ofimpending mechanical faults such as motor unbalance or misalignment.

Therefore, above-described impending mechanical fault detectiontechnique provides a pump/machine operator with an indication that thepump and motor shaft are misaligned. The technique is applicable tothree-phase induction motor driven pumps. The diagnostic function isaccomplished via spectral analysis of the notched motor current signalacquired from the motor terminals by relay sensors. No other oradditional instrumentation beyond the relay is required.

Referring now to FIGS. 21-24, the technique described with respect toFIGS. 18-20 is shown in greater detail. The technique begins in aninitialization state 212 where all flags, counters, and memory are firstcleared 214. Specifically, flags are cleared, counters are set to zero,and any previous CFS and MFS baseline buffers are cleared 214. Then, anypreviously stored FFT averaging and P_(load) averaging are cleared 216.The system then reads the rated horsepower (HP), rated motor speed(RPM), and system frequency (f_(s)) 218. Using this data 218, the systemcalculates the rated motor power (P_(rated)), the shaft frequency(f_(r)), and the number of poles of the motor (p) 220. Specifically,P_(m) is set equal to the HP*746, f_(r) is set equal to the RPMs/60, andp is set equal to twice the whole quotient of f_(s)/f_(r). The systemthen determines the frequency boundaries for motor misalignment andunbalanced load signatures 222. That is, the frequency boundaries areset as follows:F _(upper) =(1−0.4/p)*f _(s)+1.2 *f _(r)−0.1  (Eqn. 9),F _(lower)=(1+0.4/p)*f _(s)−1.2*f _(r)+1  (Eqn. 10).

The system then selects the FFT parameters 224, including a number ofpoints for each FFT iteration (N_(FFT)) and decimation factors as wellas the specific FFT resolution desired. In accordance with a preferredembodiment of the invention, the FFT resolution is selected to be equalto the quotient of f_(s)/N_(FFT). The system then sets the motor loadrange and the number of load bins to be divided over the monitored loadrange 226. Specifically, the system identifies the motor load range tobe monitored and divides the monitored load range into an even number ofload bins. The system then sets the number of iterations for FFTaveraging and the number of iterations for CFS and MFS averaging inlearning mode 228. As the final step of initialization, the system setsthresholds for CFS and MFS fault diagnoses 230. It is contemplated thatthese thresholds may be either user defined, motor parameter selected,or predefined within the relay programming such as through a lookuptable. Once the initialization is complete the system continues to themonitoring algorithm which will be described with respect to FIGS.22-24.

Referring to FIG. 22, a portion of a monitoring algorithm 232 inaccordance with the present invention is shown. The monitoring algorithm232 begins by reading the notch-filtered motor current and decimatingthe current data in preparation for FFT 234. Substantiallysimultaneously, the system monitors motor power, low pass filters themotor power data, and sets a maximum power (P_(max)) and a minimum power(P_(min)) 236. That is, traditional relay-type power metering isperformed and the data received during the metering is subjected to alow pass filter that removes frequencies below 20 Hz. Also, for each FFTframe, a P_(max) and P_(min) are set. Following these initial steps ofpower metering 236, power characteristic calculations, including a meanpower calculation (P_(mean)), a load power calculation (P_(load)), and atransient power calculation (P_(t)), are all performed 238. That is,P_(mean), P_(load), and P_(t) are calculated as follows: $\begin{matrix}{{P_{mean} = {\frac{1}{N}{\sum\limits_{i = 1}^{N}\quad{p(i)}}}},} & \left( {{Eqn}.\quad 11} \right)\end{matrix}$  P _(load) =P _(mean) /P _(rated)  (Eqn. 12),$\begin{matrix}{P_{t} = {\frac{P_{\max} - P_{\min}}{P_{rated}}.}} & \left( {{Eqn}.\quad 13} \right)\end{matrix}$

Following the power characteristics calculations 238, P_(t) is checkedto determine whether it is greater than 0.1. Then P_(load) is checked todetermine whether it is greater than 1.15. Additionally, P_(load) ischecked to determine whether it is less than 0.45. All three checks areperformed at step 240. If any of these conditions are true 242, FFTaveraging and P_(load) averaging are reset 244 and the monitoringalgorithm restarts. However, if the determinations are all negative 246,then an FFT is performed on the notched current data, which is scaled toa real value (approximately a 0.1 Hz resolution, rectangular window),and P_(load) is compared to the first data point within the power data(P1_(load)) 248. A comparison is then made to determine whetherP1_(load) is equal to zero or whether the absolute value of P1_(load)less P_(load) is greater than 0.1 at step 250. If either condition istrue 252, FFT averaging and P_(load) averaging are reset and P_(load) issaved as P1_(load) at step 254. However, if both conditions are negative256, the system bypasses the reset of the FFT averaging and P_(load)averaging and performs linear averages on the FFT data and P_(load) data258. A determination is then made as to whether such a linear averagehas been completed a sufficient number of times 260 and, if not, 262 themonitoring algorithm 232 reiterates. Specifically, a count of linearaveraging iterations is compared to a threshold value. In a preferredembodiment, the threshold value is at least 6 so that robust averagingis completed. If the linear averaging has been completed the minimumnumber of times 264, the monitoring algorithm continues as will bedescribed with respect to FIG. 23.

Referring now to FIG. 23, the monitoring algorithm 232 continues with adetermination of whether the motor being monitored has more than 8 poles266. If the motor does have more than 8 poles 268, the monitoringalgorithm 232 simply reiterates. That is, in accordance with a preferredembodiment, motors with more than 8 poles are not monitored. While in apreferred embodiment, this threshold is set at 8 poles, it iscontemplated that any number of poles may be selected as its thresholdfor comparison 266. However, if the motor has less than 8 poles 270, asecond comparison is made to determine whether the motor is a 2 polemotor 272. If the motor has two poles 274, the upper sideband range forcomparison and monitoring (F_(upper)) is set to twice the systemfrequency less 0.2 Hz at step 276. Then for all spectrum componentswithin the defined sideband range, the MFS is calculated 278 accordingto Equation 8.

However, if the motor is not a two pole motor 280, the upper sidebandranges are set 282 according to:F _(upper)=(1+2/p)f _(s)  (Eqn. 14),F_(lower)=(1−2/p)f _(s)  (Eqn. 15).

Once the upper and lower sideband ranges are defined 282, the MFS iscalculated 284 for all spectrum components within each sidebandaccording to Equation 8. The results from each sum within the sidebandsare then subjected to a linear average 286 and the MFS is stored 288.Regarding the frequency spectrum of the notch current data, medianfiltering is performed 290 at a window length of 7. Then all spectrumcomponents within the frequency spectrum of the notch current datawithin the upper and lower sidebands defined from f_(s)−25 to f_(s)−5 Hzand from f_(s)+5 to f_(s)+25 Hz, respectively, are summed and stored asa CFS 292. The monitoring algorithm then continues as will be describedwith respect to FIG. 24.

Referring now to FIG. 24, the monitoring algorithm 232 continues bydetermining two adjacent load bin index numbers (index1 and index2),within the average P_(load) based on load bin size and load range,respectively 294. That is, index1 is selected based on the load bin sizeand index2 is selected based on the load range. The system then reviewsindex1 to determine whether index1 indicates that the system is inlearning mode 296. If the system is in learning mode as indicated byindex1 at step 298, the currently calculated MFS and CFS are averagedwith previously stored CFS and MFS baseline data 300. The system thendetermines whether the baseline data has been averaged with new datafrom MFS and CFS calculations a minimum number of times at step 302. Inaccordance with one embodiment, the system determines whether the datahas been averaged at least 50 times. To set and place the number of loadbins across the various load ranges, it is contemplated that the RMSvalue of the frequency spectrum components may be averaged with thestored data and repeated at increments of 5% until the highest level ofthe load bins is reached. If sufficient averaging has not yet occurred304, the monitoring algorithm 232 reiterates. However, if the baselinedata has been averaged the minimum number of times 306, the systemchanges a flag with respect to index1 from learning mode to monitoringmode 308 and then the monitoring algorithm restarts.

However, if index 1 indicates that the system is not in learning mode310, a check is made to determine whether index2 indicates that thesystem is in learning mode 312. If index2 indicates that the system isin learning mode 314, the CFS and MFS baseline associated with index 1is loaded 316. However, if index2 indicates that the system is not inlearning mode 318, the CFS and MFS associated with both index1 andindex2 and the CFS and MFS baseline for P_(load) is interpolatedtherefrom 320. The system then sets a cavitation fault index (CFI)variable equal to the newly read CFS 316 or the interpolated CFS 318 anddivides it by the CFS baseline associated with index1 or theinterpolated index1 and index2 at step 322. A comparison of the faultindex is then made to a threshold 324. Again, it is contemplated thatthe threshold may be user defined, parameter determined, or preset andassociated with the system. If the CFI is not greater than the threshold325, the system clears the cavitation flag 326. However, if the CFI isgreater than the threshold 327 the system sets a cavitation flag 328.

The system then determines whether the current motor is greater than 4poles at step 330 and, if so 332, reiterates the monitoring algorithm.Should the motor being monitored not have greater than 4 poles 334, amechanical fault index (MFI) is reset to the recently calculated orinterpolated MFS and divided by the MFS baseline 336. The MFI is thencompared to a threshold 340, and if greater than the threshold 342, amechanical fault flag is set 344. On the other hand, if the MFI is notgreater than the threshold 346, then the mechanical fault flag iscleared 348. In either case, the monitoring algorithm 232 thenreiterates.

Referring now to FIG. 25, a block diagram of the above-describedwellness relay system 350 is shown within a motor system 352. The motorsystem 352 includes a known communications system traditionally usedwith such motor systems 352 to enable monitoring and traditionalfeedback systems 354 to communicate to an operator display 356 over acommunications bus 358. The wellness relay system 350 includes a highspeed, low-cost interconnect or interface 360, capable of allowing thewellness relay system 350 to communicate alerts and notices onto thecommunications bus 358. The interface 360 is not part of an additionalnetwork, but instead allows the wellness relay system 350 to have accessto a common back-plane used to communicate information from the wellnessrelay system 350. It is contemplated that the communications bus 358 maybe a control bus or other communications system of the motor system 352.It is further contemplated that the communications bus 358 may besimilar to a common communications bus used to connect a keyboard to apersonal computer. The communications interface 360 may be adapted to aspecific protocol of the communications bus 358 so that the informationfrom the wellness relay system 350 is available to the operator display356.

It is contemplated that the wellness relay system 350 may operatesimilarly to traditional low voltage motor control centers (LVMCC).However, as previously described, the wellness relay system 350 includesmany additional wellness detection features which distinguish it fromtraditional LVMCCs. Table II shows the available system information fromthe traditional LVMCC starter unit, typically communicated viaprogrammable logic controller (PLC) input/output modules, as well as theinformation available from the wellness relay system 350. Accordingly,the wellness relay may provide feedback regarding trip times and datesand trip causes including at least motor overload, phase unbalance, andground fault as well as impending fault condition alerts. TABLE IISYSTEM INFORMATION AVAILABILITY Wellness Traditional RelayControl/Diagnostic Function Relay System Motor Start/Stop X X Trip/ResetX X Cause of Trip Motor Overload X Phase Unbalance X Ground Fault XMotor Phase Currents (Ia, Ib, Ic) X Thermal Capacity X Time/Date of TripX CBM Wellness Fault Detection Cavitation X Mechanical X

Referring again to FIG. 25, the communications interface 360 enables thewellness relay system 350 to connect through one network adapter 362,which, in turn, allows all monitoring devices 354, 350 in one verticalstructure to be connected to only one adapter 362. The wellness relaysystem 350 receives power from a power source 364. The power source 364may also be used to power a contactor coil (not shown) and the networkadapter 362 as well as the wellness relay system 350. In accordance witha preferred embodiment, a traditional 120 VAC control power transformermight be eliminated utilizing such an architecture. Although a generalnetwork adapter 362 and communications interface 360 are shown, manynetwork adapters are contemplated.

Referring now to FIG. 26, an operator interface 366 in accordance withthe present invention is shown. The configuration of the operatorinterface 366 is designed to alert operators to specific conditions ascommunicated from the relay system. Diagnostic information 368 isavailable so that plant operators are able to quickly identify themotor's location 370, a time line 372, and any impending problem 374,such as pump cavitation or misalignment. The information communicated370-374 allows the operator to schedule required maintenance to correctan identified problem 374. Unlike traditional motor control andprotective devices, like a traditional overload relay, theabove-described relay system can be designed to annunciate an impendingfault without tripping the control circuit. FIG. 26 shows but onepossible approach for an operator interface indicating high vibration,which could lead to an impending bearing failure.

Therefore, the present invention includes a power meter including ahousing and a plurality of sensors configured to monitor operation of amotor. A processor is disposed within the housing and configured toreceive operational feedback from the plurality of sensor andproactively determine an operational wellness of the motor from theoperational feedback.

In another embodiment of the present invention, an overload relayincludes a relay housing and a power meter disposed within the relayhousing and configured to receive data from a motor and perform motorfault protection. A wellness system is disposed within the housing andconfigured to review the data and proactively determine a wellness ofthe motor to generate condition based maintenance alerts.

An alternate embodiment of the present invention has a kit that isconfigured to retrofit a relay. The kit includes a housing havingdimensions substantially similar to an overload relay and an interfaceconfigured to receive feedback from a plurality of sensors monitoring amotor. A wellness system is disposed within the housing and configuredto receive the feedback from the interface and determine whetherpreventative maintenance is required on the motor.

Another embodiment of the present invention includes a controllerconfigured to detect indicia of motor faults. The controller has aprocessor configured to determine motor parameters of a given motor,generate a set of baseline data for the given motor, and acquire currentdata from the given motor during operation. The processor is alsoconfigured to isolate sidebands within the current data, map the currentdata within the sidebands to one of a plurality of bins, and compare thecurrent data within the sidebands to baseline data from the set ofbaseline data associated with the bin. The processor is then configuredto determine a predictive fault index of the given motor prior to anactual fault occurrence.

A further embodiment of the present invention has a method of monitoringa motor for potential faults. The method includes receiving current datafrom an operating motor, performing at least one FFT on the current datato generate frequency spectrum data, and isolating sidebands within thefrequency spectrum data based on a system frequency of the operatingmotor. The method also includes accumulating spectrum components of thefrequency spectrum data within sidebands and generating baseline datafrom the accumulated spectrum components for a predetermined period.Then, after the predetermined period, the method includes comparing thespectrum components to the baseline and determining a noise patternindicative of potential faults due to pump cavitation within thespectrum components.

Another embodiment of the present invention includes a computer readablestorage medium having stored thereon a computer program. The computerprogram includes instructions which, when executed by at least oneprocessor, cause the at least one processor to determine a load on amotor and receive operational current data from the motor. The at leastone processor is also caused to perform at least two FFTs on theoperational current data to generate frequency spectrum data, averagethe frequency spectrum data, and define sidebands of the systemfrequency of the motor. The at least one processor is also caused to sumthe frequency spectrum data within sidebands to generate a cavitationfault index, map the cavitation fault index to a load bin from aplurality of load bins based on the load on the motor, and average thecavitation fault index with baseline data associated with the load binif in a learning mode. However, if not in the learning mode, the atleast one processor is caused to compare the cavitation fault index tothe baseline data associated with the load bin to determine an impendingcavitation fault before a fault occurrence.

An additional embodiment of the present invention includes a controllerconfigured to detect indicia of impending mechanical motor faults. Thecontroller includes a processor configured to determine motor parametersof a given motor including a load, generate a set of baseline data forthe given motor, and acquire current spectrum data from the given motorduring operation. The processor is also caused to map at least one froma plurality of load bins based on the load and generate a mechanicalfault signature from the current spectrum. The processor is caused tocompare the mechanical fault signature to baseline data from the set ofbaseline data corresponding to the mapped bin and determine amplitudevariances within the mechanical fault signature indicative of animpending mechanical fault prior to an actual mechanical faultoccurrence.

In another embodiment of the present invention, a method of detectingimpending mechanical faults includes generating baseline data for theoperating motor and receiving current data from an operating motor. Themethod also includes performing at least one FFT on the current data togenerate frequency spectrum data, selecting system frequency sidebandswithin the frequency spectrum data, and summing the frequency spectrumdata within the system frequency sidebands. The method includescomparing the summed frequency spectrum data to a portion of thebaseline data and determining amplitude variances within a component ofa running shaft speed of the motor indicative of prospective faults dueto at least one of motor misalignment and unbalance.

An alternate embodiment of the present invention has a computer readablestorage medium having stored thereon a computer program comprisinginstructions which, when executed by at least one processor, cause theat least one processor to receive operational current data from a motor.The at least one processor is also caused to perform at least two FFTson the operational current data to generate frequency spectrum data,average the frequency spectrum data, and generate a mechanical faultsignature from frequency spectrum data. If in a learning mode, the atleast one processor is caused to compile a baseline from the mechanicalfault index. However, if not in the learning mode, the at least oneprocessor is caused to determine a load of the motor and map the averagepower of the motor to a load bin based on the load of the operatingmotor. The processor is also caused to compare a portion of the baselinecorresponding to the mapped load bin to the mechanical fault signatureand determine an impending mechanical fault before a fault occurrence.

The present invention has been described in terms of the preferredembodiment, and it is recognized that equivalents, alternatives, andmodifications, aside from those expressly stated, are possible andwithin the scope of the appending claims.

1. A controller configured to detect indicia of impending mechanicalmotor faults and having a processor configured to: determine motorparameters of a given motor including a load; generate a set of baselinedata for the given motor; acquire current spectrum data from the givenmotor during operation; map the current spectrum data to at least one ofa plurality of load bins based on the load; generate a mechanical faultsignature from the current spectrum; compare the mechanical faultsignature to baseline data from the set of baseline data correspondingto the mapped bin; and determine amplitude variances within themechanical fault signature indicative of an impending mechanical faultprior to an actual mechanical fault occurrence.
 2. The controller ofclaim 1 wherein the processor is further configured to isolate sidebandswithin the current spectrum data and sum the current spectrum datawithin the sidebands to generate the mechanical fault signature.
 3. Thecontroller of claim 1 wherein the processor is further configured toselect the desired sidebands selected based on a system frequency of thegiven motor.
 4. The controller of claim 1 wherein the processor isfurther configured to dynamically notch filter the current spectrum datato remove a fundamental frequency of the given motor.
 5. The controllerof claim 4 wherein the processor is further configured to track thefundamental frequency of the given motor over an operational range foruse with an inverter.
 6. The controller of claim 1 wherein the processoris further configured to digitize and decimate the current spectrum datato acquire a desired resolution for comparison with the baseline data.7. The controller of claim 1 wherein the processor is further configuredto receive raw current data and compute at least one fast FourierTransform (FFT) to generate the current spectrum data.
 8. The controllerof claim 7 wherein the processor is further configured to average thecurrent spectrum data to stabilize the current frequency spectrum data.9. The controller of claim 1 wherein the processor is further configuredto generate the mechanical fault signature by calculating a root meansquare (RMS) of the current spectrum data.
 10. The controller of claim 1wherein the processor is further configured to determine a number ofpoles of the given motor.
 11. The controller of claim 1 wherein theprocessor is further configured to acquire power data from the givenmotor and map the power data into the load bin to determine a monitoringstate of the mapped load bin.
 12. The controller of claim 11 wherein ifthe monitoring state of the mapped load bin is a learning mode, theprocessor is further configured to generate the set of baseline data bycontinuously averaging the mechanical fault signature of the given motorunder known healthy conditions.
 14. The controller of claim 1 whereinthe processor is further configured to determine a noise patternindicative of potential motor misalignment from the fault signature ofthe given motor by detecting amplitude variances within motor shaftrunning speed components within the current spectrum data.
 15. Thecontroller of claim 14 wherein the processor is further configured tocommunicate a proactive alert over a communications bus upon determiningamplitude variances in the shaft running speed components greater than athreshold.
 16. The controller of claim 15 wherein the processor isfurther configured to set the threshold according to one of a userselected threshold, a dynamically learned threshold, and a lookup table.17. The controller of claim 1 wherein the processor is furtherconfigured to perform at least one of a root sum square (RSS) valuecalculation and a polynomial estimation to compensate for energy leakagewithin the current data.
 18. The controller of claim 17 wherein theprocessor is further configured to estimate an instantaneous amplitudeof components of running shaft speed of the given motor as a function ofneighboring components of running shaft speed of the given motor toperform the polynomial estimation.
 19. The controller of claim 17wherein the processor is further configured to perform the RSS valuecalculation for a plurality of frequency components in the desiredsidebands surrounding a shaft frequency of the given motor.
 20. Thecontroller of claim 1 wherein the processor is further configured tocommunicate preventative maintenance alerts over a communications bus toan operator display upon detecting indicia of at least one of motormisalignment and motor imbalance within the motor fault signature. 21.The controller of claim 1 wherein the processor is further configuredto: acquire power data from the given motor during operation; generate amechanical fault signature of the given motor from the power data; anddetermine indicia of at least one of motor misalignment and motorimbalance within the motor fault signature.
 22. The controller of claim21 wherein the power data includes instantaneous power data.
 23. Amethod of detecting impending mechanical faults comprising: generatingbaseline data for the operating motor; receiving current data from anoperating motor; performing at least one FFT on the current data togenerate frequency spectrum data; selecting system frequency sidebandswithin the frequency spectrum data; summing the frequency spectrum datawithin the system frequency sidebands; comparing the summed frequencyspectrum data to a portion of the baseline data; and determiningamplitude variances within a component of a running shaft speed of themotor indicative of prospective faults due to at least one of motormisalignment and unbalance.
 24. The method of claim 23 furthercomprising: determining an average power of the operating motor;determining a load of the operating motor; mapping the average power ofthe operating motor to the a load bin based on the load of the operatingmotor; and selecting a portion of the baseline data for comparison tothe summed frequency data based on the mapped load bin.
 25. The methodof claim 23 further comprising performing a plurality of FFTs on thecurrent data and average results from the plurality of FFTs to generatethe frequency spectrum data.
 26. The method of claim 23 furthercomprising comparing the frequency spectrum data to the baseline datawithin the sidebands of a shaft running frequency of the operating motorto generate a mechanical fault index indicative of a probability ofprospective faults due to at least one of motor misalignment andunbalance.
 27. The method of claim 23 further comprising performing atleast one of an RSS value calculation and a polynomial estimation tocompensate for energy leakage within the current data.
 28. The method ofclaim 27 further comprising estimating an instantaneous amplitude ofrunning shaft speed components as a function of neighboring runningshaft speed components to perform polynomial estimation.
 29. The methodof claim 28 wherein the neighboring running shaft speed componentsinclude at least one upper running shaft speed component and at leastone lower running shaft speed component.
 30. The method of claim 27further comprising performing the RSS value calculation for a pluralityof frequency components in the system frequency sidebands.
 32. Themethod of claim 23 further comprising communicating a wellness alertupon detecting amplitude variances indicative of future mechanicalfaults.
 33. The method of claim 32 further comprising communicating thewellness alert to an operator counsel over a communications bus of asystem including the operating motor.
 34. A computer readable storagemedium having stored thereon a computer program comprising instructionswhich, when executed by at least one processor, cause the at least oneprocessor to: receive operational current data from a motor; perform atleast two FFTs on the operational current data to generate frequencyspectrum data; average the frequency spectrum data; generate amechanical fault signature from frequency spectrum data; if in alearning mode, compile a baseline from the mechanical fault index; andif not in the learning mode: determine a load of the motor; map theaverage power of the motor to the a load bin based on the load of theoperating motor; compare a portion of the baseline corresponding to themapped load bin to the mechanical fault signature; and determine animpending mechanical fault before a fault occurrence.
 35. The computerprogram of claim 34 wherein the processor is further caused to selectsidebands within the frequency spectrum data around a system frequencyof the motor and determine amplitude variances within the sidebandsindicative of impending mechanical faults
 36. The computer program ofclaim 34 wherein the processor is further caused to communicate awellness alert upon detecting amplitude variances indicative of futuremechanical faults.
 37. The computer program of claim 36 wherein theprocessor is further caused to communicate the wellness alert to anoperator counsel over a communications bus of a system including themotor.
 38. The computer program of claim 34 wherein the processor isfurther caused to compensate for energy leakage in the current data. 39.The computer program of claim 38 wherein the processor is further causedto perform at least one of an RSS value calculation and a polynomialestimation to compensate for the energy leakage within the current data.40. The computer program of claim 39 wherein the processor is furthercaused to estimate an instantaneous amplitude of running shaft speedcomponents within the frequency spectrum data as a function ofneighboring running shaft speed components to perform polynomialestimation.
 41. The computer program of claim 39 wherein the processoris further caused to perform the RSS value calculation for a pluralityof frequency components in a selected frequency band surrounding arunning frequency of the motor.
 41. The computer program of claim 34wherein the processor is further caused to: compile baseline power data;receive operational power data from the motor; perform at least two FFTson the operational power data to generate power spectrum data; averagethe power spectrum data; generate a mechanical fault signature frompower spectrum data; compare a portion of the baseline power datacorresponding to a mapped load bin to the averaged power data; anddetermine an impending mechanical fault before a fault occurrence.